This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.

1. By: Khalid Imran
Big Data :
Technology Stack

2. Agenda
▪ Big Data Stack : In a Nutshell
▪ Data Layer
▪ Data Processing Layer
▪ Data Ingestion Layer
▪ Data Presentation Layer
▪ Operations & Scheduling Layer
▪ Security & Governance
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3. Big Data Technology Stack : In a nutshell
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4. Data Layer
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Hadoop Distributed File System (HDFS)
HDFS is a scalable, fault-tolerant Java based distributed file system that is used for storing
large volumes of data in inexpensive commodity hardware.
Amazon Simple Storage Service (S3)
S3 is a cloud based scalable, distributed file system offering from Amazon. It can be
utilized as the data layer in big data applications, coupled with other required
components.
IBM General Parallel File System (GPFS) / Spectrum Scale
GPFS is a high-performance clustered file system developed by IBM.

5. Data Processing Layer
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Hadoop MapReduce
Hadoop Map/Reduce is a software framework for distributed processing of large data sets on
compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The
framework takes care of scheduling tasks, monitoring them and re-executing any failed tasks. A
MapReduce job usually splits the input data-set into independent chunks which are processed by
the map tasks in a completely parallel manner. The framework sorts the outputs of the maps,
which are then input to the reduce tasks. Typically both the input and the output of the job are
stored in a file-system.
Apache Pig
Pig is a high-level platform for creating MapReduce programs used with Hadoop. Apache Pig
allows Apache Hadoop users to write complex MapReduce transformations using a simple
scripting language called Pig Latin. Pig translates the Pig Latin script into MapReduce so that it can
be executed on the data. Pig Latin can be extended using UDF (User Defined Functions) which the
user can write in Java, Python, JavaScript, Ruby or Groovy and then call directly from the language.

6. Data Processing Layer
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Apache Hive
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data
summarization, query, and analysis. Hive is used to explore, structure and analyze data, then turn
it into actionable business insight. Apache Hive supports analysis of large datasets stored in
Hadoop's HDFS and compatible file systems such as Amazon S3 file system. It provides an SQL-like
language called HiveQL with schema on read and transparently converts queries to map/reduce,
Apache Tez and Spark jobs.
Apache HBase
HBase is an open-source NoSQL database that provides real-time read/write access to large
datasets with extremely low latency as well as fault tolerance. HBase runs on top of HDFS. HBase
provides a strong consistency model, and range-based partitioning. Reads, including range-based
reads, tend to scale much better on HBase, whereas writes do not scale as well as they do on
Cassandra..

7. Data Processing Layer
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Apache Cassandra
Cassandra is another open-source distributed NoSQL database. It is highly scalable, fault tolerant
and can be used to manage huge volumes of data. Cassandra's consistency model is based on
Amazon's Dynamo: it provides eventual consistency. This is very appealing for some applications
where you want to guarantee the availability of writes. Similarly, Cassandra tends to provide very
good write scaling.
Apache Storm
Storm is a distributed real-time computation system for processing large volumes of high-velocity
data. Storm makes it easy to reliably process unbounded streams of data, doing for real-time
processing what Hadoop did for batch processing.
Apache Solr
Apache Solr is the open source platform for searches of data stored in HDFS in Hadoop. Solr
powers the search and navigation features of many of the world’s largest Internet sites, enabling
powerful full-text search and near real-time indexing. Apache Solr can be used for rapidly finding
tabular, text, geo-location or sensor data that is stored in Hadoop.

8. Data Processing Layer
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Apache Spark
Apache Spark is an open source cluster computing framework for large-scale data processing.
Studies have shown that Spark can run up to 100x faster than Hadoop MapReduce in memory, or
10x faster on disk for program execution. It provides in-memory computations for increased speed
and data processing over MapReduce. It runs on top of existing Hadoop cluster and can access
Hadoop data store (HDFS), as well as also process structured data from Hive and streaming data
from HDFS, Flume, Kafka, Twitter and other sources.
Apache Mahout
Apache Mahout is a library of scalable machine-learning algorithms that can be implemented on
top of Apache Hadoop and it utilizes the MapReduce paradigm. Machine learning is a discipline of
artificial intelligence focused on enabling machines to learn without being explicitly programmed,
and it is commonly used to improve future performance based on previous outcomes. Mahout
provides the tools and algorithms to automatically find meaningful patterns in those big data sets
stored in the HDFS.

9. Data Ingestion Layer
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Apache Flume
Apache Flume is a distributed, reliable, and available service for efficiently collecting,
aggregating, and moving large amounts of streaming data (e.g. application logs, sensor and
machine data, geo-location data and social media) into the HDFS. It has a simple and flexible
architecture based on streaming data flows; and is robust and fault tolerant with comes with
configurable reliability mechanisms for failover and recovery.
Apache Kafka
Kafka is a high throughput distributed messaging system. Kafka maintains feeds of messages
in categories called topics. Producers are processes that publish messages to a Kafka topic.
Consumers are processes that subscribe to topics and process the feed of published
messages. Kafka is run as a cluster comprising of one or more servers each of which is called
a broker.
Apache Sqoop
Apache Sqoop is a tool designed to transfer data between Hadoop and relational databases or
mainframes. Sqoop can be used to import data from a RDBMS or a mainframe into HDFS,
transform the data using Hadoop MapReduce, and then export the data back into an RDBMS.

10. Data Presentation Layer
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Kibana
Kibana is an analytics and visualization plugin that works with ElasticSearch. It provides real-time
summary and charting of streaming data. The visualization capabilities it provides allow users to
different charts, plots and maps of large volumes of data.

11. Operations & Scheduling Layer
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Ambari
Ambari is an open framework that helps in provisioning, managing and monitoring of Apache
Hadoop clusters. It simplifies the deployment and maintenance of hosts. Ambari also includes an
intuitive web interface that allows one to easily provision, configure and test all the Hadoop
services and core components. It also comes with the powerful Ambari Blueprints API that can be
utilized for automating cluster installations without any user intervention.
Apache Oozie
Apache Oozie provides operational service capabilities for a Hadoop cluster, specifically around
job scheduling within the cluster. Oozie is a Java based web application that is primarily used to
schedule Apache Hadoop jobs. Oozie can combine multiple jobs sequentially into one logical unit
of work. It can be integrated with the Hadoop stack, and supports Hadoop jobs for various Apache
tools such as MapReduce, Apache Pig, Apache Hive, and Apache Sqoop.
Apache ZooKeeper
Apache ZooKeeper provides operational services for a Hadoop cluster. It provides a distributed
configuration service, a synchronization service and a naming registry for distributed systems that
can use Zookeeper to store and mediate updates to important configuration information.

12. About Me – Khalid Imran
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A tester by passion, I’ve spent the past 16+ years testing disparate systems, learning new domains, developing
innovative solutions, designing test strategies, challenging conventional methods, proving new techniques and
embracing emerging tools & technologies. The breadth and depth of my experience cuts across functional testing,
non-functional testing, manual, automation, test and project execution methodologies, licensed and open stack
tools, platforms, devices, programming languages, custom-built test harnesses and utilities, delivery management,
client engagement, on-site, off-shore team dynamics and more.
I am currently heading the 1400+ QA strong testing practice at Cybage as a QA Evangelist. I manage the Testing
Centre of Excellence (TCoE), lead a team of architects and specialists and assist in deliveries across the organization,
pre-sales and business development, solutioning and consultancy, training and process improvement group. I hold
multiple certifications namely: CSQA, CSM and CPISI.
I welcome any questions or feedback you may have on this presentation.